code: def test_can_construct_from_params(
      params_dict, transformer_embeddings
  ):
      embeddings = transformer_embeddings.embeddings
      assert (
          embeddings.word_embeddings.num_embeddings
          == params_dict["vocab_size"]
      )
      assert (
          embeddings.word_embeddings.embedding_dim
          == params_dict["embedding_size"]
      )
      assert (
          embeddings.word_embeddings.padding_idx
          == params_dict["pad_token_id"]
      )
  
      assert (
          embeddings.position_embeddings.num_embeddings
          == params_dict["max_position_embeddings"]
      )
      assert (
          embeddings.position_embeddings.embedding_dim
          == params_dict["embedding_size"]
      )
  
      assert (
          embeddings.token_type_embeddings.num_embeddings
          == params_dict["type_vocab_size"]
      )
      assert (
          embeddings.token_type_embeddings.embedding_dim
          == params_dict["embedding_size"]
      )
  
      assert (
          transformer_embeddings.layer_norm.normalized_shape[0]
          == params_dict["embedding_size"]
      )
  
      assert transformer_embeddings.dropout.p == params_dict["dropout"]
  
imports: import copy
  import pytest
  import torch
  from torch.testing import assert_allclose
  from transformers import AutoModel
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_bert import BertEmbeddings
  from transformers.models.albert.configuration_albert import AlbertConfig
  from transformers.models.albert.modeling_albert import AlbertEmbeddings
  from allennlp.common import Params, FromParams
  from allennlp.modules.transformer import TransformerEmbeddings, ImageFeatureEmbeddings, TransformerModule
label_desc: Use image node_id 4 for calling a global function with example usage: test_can_construct_from_params(params_dict, transformer_embeddings) without return types